How AI and Machine Learning Drive Cyber Security in Fintech

Fintechs are rich, vulnerable targets for hostile actors. Here’s how we’re pioneering new technologies to stop threats at their outset

Given the light-speed advancement of AI-assisted applications for consumers, it’s understandable that companies in the growing Financial Technology space are anxious to deploy faster and smarter machine-learning tools to deter cyber crime. Fintech attracted a record $112 billion in investments in 2018 as executives pursued their industry-equivalent to a driverless car: smart, intelligent systems that make life and business better. In Fintech, as in transportation, safety is tantamount. And Fintech security hinges on big data analytics and machine learning that are perhaps even more challenging than an autopilot that avoids a crash in a freeway pileup.

As a subset of the financial services sector, Fintechs are rich, vulnerable targets for hostile actors. Companies require a multilayered arsenal of defenses to keep their information secret and safe. By one estimate, financial businesses fall prey to cyber security attacks 300 times more often than those in other industries. Last year the World Economic Forum cited Fintech concerns in particular, declaring cyber security “the number-one threat to the financial services industry and its infrastructure.”

It's a battleground, and Symantec’s Center for Advanced is leading the defensive charge. We’re translating the latest AI and machine learning breakthroughs into powerful product advantages. As we noted in a 2016 blog that accompanied the launch of Symantec Endpoint Protection 14, Symantec offers the most advanced machine learning available for endpoint security based on advanced feature engineering and ensembling. SEP is ranked highest for execution in Gartner’s 2018 Magic Quadrant. Symantec’s Managed Endpoint Detection and Response Service beefs up SEP and extends clients’ in-house skillsets with world-class dedicated experts who specialize in hunting and investigating early indicators of threats, whether the danger is unfolding on-site or in the cloud.

Last year the World Economic Forum cited Fintech concerns in particular, declaring cyber security “the number-one threat to the financial services industry and its infrastructure.”

Of course, great AI requires great data. Our models swiftly analyze one of the world’s largest non-governmental collections of multifactor telemetry to identify potential threats and unusual behavioral patterns. On any given day, we ingest more than 2 petabytes of data, generated from billions of files and messages, and trillions of network connections. Designed to operate at scale, SEP alone tracks threat and attack data across 175 million endpoints and monitors 57 million attack sensors in real time, minute-by-minute.

Globally, businesses are in the midst of a Cambrian explosion of AI for convenient consumer technologies, but even the most popular consumer platforms are prone to bugs and fail rates that neither the financial services sector nor the cyber security industry can tolerate. In cyber security, we can’t afford to have a single point of failure. We’re also in a constant race against bad actors, and continually fine-tuning products in response to emerging threats.

That said, machine learning in cyber security is evolving. It’s now possible to build a product, and, as the threat landscape changes, add defenses incrementally, without having to tear it down and rebuild it. At Symantec, we’re also accruing proprietary techniques for combining predictions across multiple models, in some cases enabling machines to arrive at the correct prediction even before the developers.

Ultimately, beyond increasing our velocity in defense response and remediation, we should aspire to an epidemiological view. Long-term, through advanced machine learning, we can halt threats at their onset, and even, possibly, inoculate against them.